ArticlePharmaceutics2020
A Physiologically-Based Pharmacokinetic (PBPK) Model Network for the Prediction of CYP1A2 and CYP2C19 Drug-Drug-Gene Interactions with Fluvoxamine, Omeprazole, S-mephenytoin, Moclobemide, Tizanidine, Mexiletine, Ethinylestradiol, and Caffeine.
Article in Pharmaceutics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 25 citations in OpenAlex.
- Physiologically Based Pharmacokinetic (PBPK) Modeling of Antidepressants: A Scoping Review of Existing Models, Applications, and Software Tools.Clinical pharmacokinetics · 2026Article
- Observational
- Rosuvastatin PBPK Modeling: Incorporating Liver Concentrations and Effects of Ethnicity, Genetic Polymorphisms, Lactone Formation, DDI and Pregnancy.CPT: pharmacometrics & systems pharmacology · 2025Article
- Drug Interaction PBPK Modeling: Review of the Literature Exposes the Need for Increased Verification of Model Inputs and Outputs as Part of Credibility Assessment.Clinical and translational science · 2025Review
- A Comprehensive CYP2D6 Drug-Drug-Gene Interaction Network for Application in Precision Dosing and Drug Development.Clinical pharmacology and therapeutics · 2025Article
- A new paradigm for drug discovery in the treatment of complex diseases: drug discovery and optimization.Chinese medicine · 2025Review
- Disulfidptosis links the pathophysiology of ulcerative colitis and immune infiltration in colon adenocarcinoma.Scientific reports · 2025Article
- Utilizing Physiologically Based Pharmacokinetic Models to Support Rational Medication in Chinese Elderly Population.Drug design, development and therapy · 2025Article
- Applied physiologically-based pharmacokinetic modeling to assess uridine diphosphate-glucuronosyltransferase-mediated drug-drug interactions for Vericiguat.CPT: pharmacometrics & systems pharmacology · 2024Article
- Physiologically Based Pharmacokinetic Modeling to Simulate CYP3A4-Mediated Drug-Drug Interactions for Pyrotinib.Advances in therapy · 2023Article
- Physiologically-based pharmacokinetic modeling of quinidine to establish a CYP3A4, P-gp, and CYP2D6 drug-drug-gene interaction network.CPT: pharmacometrics & systems pharmacology · 2023Article
- Development and Evaluation of a Physiologically Based Pharmacokinetic Model for Predicting Haloperidol Exposure in Healthy and Disease Populations.Pharmaceutics · 2022Article
- Prediction of CYP-mediated DDIs involving inhibition: Approaches to address the requirements for system qualification of the Simcyp Simulator.CPT: pharmacometrics & systems pharmacology · 2022Article
- Article
- Evaluation of the drug-drug interaction potential of treosulfan using a physiologically-based pharmacokinetic modelling approach.British journal of clinical pharmacology · 2022Article
- Disease-drug and drug-drug interaction in COVID-19: Risk and assessment.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2021Review
- A generic framework for the physiologically-based pharmacokinetic platform qualification of PK-Sim and its application to predicting cytochrome P450 3A4-mediated drug-drug interactions.CPT: pharmacometrics & systems pharmacology · 2021Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Physiologically-based pharmacokinetic (PBPK) modeling is a well-recognized method for quantitatively predicting the effect of intrinsic/extrinsic factors on drug exposure. However, there are only few verified, freely accessible, modifiable, and comprehensive drug-drug interaction (DDI) PBPK models. We developed a qualified whole-body PBPK DDI network for cytochrome P450 (CYP) CYP2C19 and CYP1A2 interactions. Template PBPK models were developed for interactions between fluvoxamine, S-mephenytoin, moclobemide, omeprazole, mexiletine, tizanidine, and ethinylestradiol as the perpetrators or victims. Predicted concentration-time profiles accurately described a validation dataset, including data from patients with genetic polymorphisms, demonstrating that the models characterized the CYP2C19 and CYP1A2 network over the whole range of DDI studies investigated. The models are provided on GitHub (GitHub Inc., San Francisco, CA, USA), expanding the library of publicly available qualified whole-body PBPK models for DDI predictions, and they are thereby available to support potential recommendations for dose adaptations, support labeling, inform the design of clinical DDI trials, and potentially waive those.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.